There is a demand for effective clustering methods, especially given the increasing complexity of data scenarios in modern applications. Motivated by the limitations of traditional clustering algorithms, particularly in handling noise, imbalanced data, and large-scale datasets, this work introduces TRUNC, a Transfer Learning Unsupervised Network for Data Clustering. TRUNC leverages a bio-inspired approach, utilizing a single-layer feed-forward winner-takes-all neural network enhanced with transfer learning to optimize clustering performance. The proposed algorithm is evaluated across a range of synthetic and real-world datasets, demonstrating its robustness and performance over conventional and state-of-the-art clustering methods. Key contributions include the integration of transfer learning into the clustering process, a detailed sensitivity analysis of the TL parameter, and extensive experimentation that confirms the efficiency and generalization capability of TRUNC. Results indicate that TRUNC improves clustering quality and convergence speed, offering a competitive and scalable solution for various clustering tasks. This proposal opens new avenues for the integration of TRUNC’s principles with other bio-inspired algorithms and its application across diverse domains.